Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery

Journal of Applied Remote Sensing

Volumn 8, Issue 1, 2014, Pages

  Lang, Haitao ^a,b   Zhang, Jie ^b,c   Zhang, Ting ^b,c   Zhao, Di ^a   Meng, Junmin ^b,c  

^a BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b Ministry of Natural Resources Guangxi)   (China)

^c FIRST INSTITUTE OF OCEANOGRAPHY   (China)

Author keywords

fuzzy c means; gray level co occurrence matrix; image moments; polarimetric synthetic aperture radar; ship detection and recognition; support vector machine

Indexed keywords

C (PROGRAMMING LANGUAGE); CLUSTERING ALGORITHMS; FUZZY CLUSTERING; FUZZY SYSTEMS; POLARIMETERS; REMOTE SENSING; SHIPS; SUPPORT VECTOR MACHINES; SYNTHETIC APERTURE RADAR; TEXTURES; TRACKING RADAR;

FUZZY C MEAN; GRAY LEVEL CO-OCCURRENCE MATRIX; IMAGE MOMENTS; POLARIMETRIC SYNTHETIC APERTURE RADARS; SHIP DETECTION;

RADAR IMAGING;

EID: 84901703232     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.8.083623     Document Type: Article

References (44)

1. H. Greidanus, "Satellite imaging for maritime surveillance of the European seas," RemoteSens. Eur. Seas. 22, 343-358 (2008), http://dx.doi.org/10.1007/978-1-4020-6772-3-26.
2. S. McCandless and C. R. Jackson, "Principles of Synthetic Aperture Radar, Marine User'sManual," in Synthetic Aperture Radar, US Department of Commerce, pp. 1-23 (2004).
3. G. Goldstein, "False-alarm regulation in log-normal and Weibull clutter," IEEE Trans.Aerospace Electron. Syst. AES-9(1), 84-92 (1973), http://dx.doi.org/10.1109/TAES.1973.309705.
4. P. Vachon et al., "Validation of ship detection by the RADARSAT synthetic aperture radarand the ocean monitoring workstation," Can. J. Remote Sens. 26(3), 200-212 (2000). (Pubitemid 32321842)
5. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput.Vision 60(2), 91-110 (2004), http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94.
6. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in IEEEComputer Society Conf. Computer Vision and Pattern Recognition, San Diego, California,Vol. 1, pp. 886-893, IEEE, New York (2005). (Pubitemid 43897286)
7. L. Fei-Fei and P. Perona, "A Bayesian hierarchical model for learning natural scene categories,"in IEEE Computer Society Conf. on Computer Vision and PatternRecognition, CVPR 2005, IEEE (2005).
8. G. Margarit and A. Tabasco, "Ship classification in single-PolSAR images based on fuzzylogic," IEEE Trans. Geosci. Remote Sens. 49(8), 3129-3138 (2011), http://dx.doi.org/10.1109/TGRS.2011.2112371.
9. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification,"IEEE Trans. Syst. Man Cybernetics SMC-3(6), 610-621 (1973), http://dx.doi.org/10.1109/TSMC.1973.4309314.
10. D. J. Crisp, The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery,DTIC Document, Defence Science And Technology Organisation Salisbury (Australia) InfoSciences Lab (2004).
11. P. Zamperoni, "Image enhancement," Adv. Imaging Electron Phys. 92, 1-77 (1995), http://dx.doi.org/10.1016/S1076-5670(08)70006-5.
12. J. C. Curlander and R. N. McDonough, Synthetic Aperture Radar-Systems and SignalProcessing, John Wiley & Sons, Inc., New York (1991).
13. A. M. Guarnieri, "Adaptive removal of azimuth ambiguities in SAR images," IEEE Trans.Geosci. Remote Sens. 43(3), 625-633 (2005), http://dx.doi.org/10.1109/TGRS.2004.842476. (Pubitemid 40320285)
14. C. Wang, "Ship detection after removal of ambiguities by using PolSAR images," in Int.Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM),Xiamen, pp. 1-4, IEEE, New York (2011).
15. C. Castillo-Rubio, S. Llorente-Romano, and M. Burgos-Garcia, "Robust SVA method forevery sampling rate condition," IEEE Trans. Aerosp. Electron. Syst. 43(2), 571-580 (2007),http://dx.doi.org/10.1109/TAES.2007. 4285354. (Pubitemid 47308048)
16. K. Kulpa et al., "SAR image enhancement by dominant scatterer removal," in IET Int. Conf.on Radar Systems, Edinburgh, UK, pp. 1-5, IET, New York (2007).
17. L. M. Novak, M. C. Burl, and W. Irving, "Optimal polarimetric processing for enhancedtarget detection," IEEE Trans. Aerosp. Electron. Syst. 29(1), 234-244 (1993), http://dx.doi.org/10.1109/7.249129. (Pubitemid 23643430)
18. M. Liao et al., "Using SAR images to detect ships from sea clutter," IEEE Geosci. RemoteSens. Lett. 5(2), 194-198 (2008), http://dx.doi.org/10.1109/LGRS.2008.915593.
19. J. Carretero-Moya et al., "Statistical analysis of a high-resolution sea-clutter database,"IEEE Trans. Geosci. Remote Sens. 48(4), 2024-2037 (2010), http://dx.doi.org/10.1109/TGRS.2009.2033193.
20. A. Blake, D. Blacknell, and C. J. Oliver, "High-resolution SAR clutter textural analysis andsimulation," in Satellite Remote Sensing II, Paris, pp. 101-108, SPIE, Western Australia(1995).
21. X. Qin et al., "A CFAR detection algorithm for generalized gamma distributed backgroundin high-resolution SAR images," IEEE Geosci. Remote Sens. Lett. 10(4), 806-810 (2013),http://dx.doi.org/10.1109/LGRS.2012.2224317.
22. M. Tello, C. López-Martínez, and J. J. Mallorqui, "A novel algorithm for ship detection inSAR imagery based on the wavelet transform," IEEE Geosci. Remote Sens. Lett. 2(2), 201-205 (2005), http://dx.doi.org/10.1109/LGRS.2005.845033. (Pubitemid 40680175)
23. R. Ringrose and N. Harris, "Ship detection using polarimetric SAR data," in SAR workshopon CEOS Committee on Earth Observation Satellites, Toulouse, France, Vol. 450, pp. 687,European Space Agency, Paris (2000).
24. R. Touzi, R. K. Raney, and F. Charbonneau, "On the use of permanent symmetric scatterersfor ship characterization," IEEE Trans. Geosci. Remote Sens. 42(10), 2039-2045 (2004),http://dx.doi.org/10.1109/TGRS.2004.834654.
25. H. Li, Y. He, and W. Wang, "Improving ship detection with polarimetric SAR based onconvolution between co-polarization channels," Sensors 9(2), 1221-1236 (2009), http://dx.doi.org/10.3390/s90201221.
26. T. Liu, G. A. Lampropoulos, and C. Fei, "CFAR ship detection system using polarimetricdata," in Radar Conference, RADAR'08, pp. 1-4, IEEE, New York (2008).
27. R. Shirvany, M. Chabert, and J.-Y. Tourneret, "Comparison of ship detection performancebased on the degree of polarization in hybrid/compact and linear dual-pol SAR imagery," inIEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC,pp. 3550-3553, IEEE, New York (2011).
28. J. Stastny et al., "A novel adaptive synthetic aperture radar ship detection system," inOCEANS, pp. 1-7 (2011).
29. D. Pastina, F. Fico, and P. Lombardo, "Detection of ship targets in COSMO-SkyMed SARimages," IEEE Radar Conference (RADAR), pp. 928-933, IEEE, New York (2011).
30. S. Brusch et al., "Ship surveillance with TerraSAR-X," IEEE Trans. Geosci. Remote Sens.49(3), 1092-1103 (2011), http://dx.doi.org/10.1109/ TGRS.2010.2071879.
31. Y. Wang and H. Liu, "A hierarchical ship detection scheme for high-resolution SARimages," IEEE Trans. Geosci. Remote Sens. 50(10), 4173-4184 (2012), http://dx.doi.org/10.1109/TGRS.2012.2189011.
32. G. Margarit and J. J. Mallorqui, "Scattering-based model of the SAR signatures of complextargets for classification applications," Int. J. Navig. Observation 2008, 1-11 (2008), http://dx.doi.org/10.1155/2008/426267.
33. G. Margarit et al., "Exploitation of ship scattering in polarimetric SAR for an improvedclassification under high clutter conditions," IEEE Trans. Geosci. Remote Sens. 47(4),1224-1235 (2009), http://dx.doi.org/10.1109/ TGRS.2008.2008721.
34. G. Margarit, J. J. Mallorqui, and X. Fabregas, "Single-pass polarimetric SAR interferometryfor vessel classification," IEEE Trans. Geosci. Remote Sens. 45(11), 3494-3502 (2007),http://dx.doi.org/10.1109/TGRS. 2007.897437. (Pubitemid 350025453)
35. G. Saur and M. Teutsch, "SAR signature analysis for TerraSAR-X-based ship monitoring"Proc. SPIE 7830, 78301O (2010), http://dx.doi.org/10.1117/ 12.866836.
36. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, KluwerAcademic Publishers, Netherland (1981).
37. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of thespatial envelope," Int. J. Comput. Vision, 42(3), 145-175 (2001), http://dx.doi.org/10.1023/A:1011139631724. (Pubitemid 32680801)
38. Y. Su, M. Allan, and F. Jurie, "Improving object classification using semantic attributes," inProc. British Machine Vision Conf., Aberystwyth, pp. 26.1-26.10, IEEE, New York (2010).
39. H. Lang and H. Ling, "Classifying covert photographs," in IEEE Conf. Computer Visionand Pattern Recognition (CVPR), Providence, Rhode Island, pp. 1178-1185, IEEE, NewYork (2012).
40. W.-T. Chen et al., "Ship recognition in high resolution SAR imagery based on feature selection,"in Int. Conf. Computer Vision in Remote Sensing (CVRS), pp. 301-305, IEEE, NewYork (2012).
41. C.-C. Chang and C.-J. Lin, "LIBSVM: a library for support vector machines," ACMTransactions on Intelligent Systems and Technology (TIST), 2(3), 1-27 (2011).
42. C. Duan, W. Hu, and X. Du, "SAR image based geometrical feature extraction of ships," inIEEE Int. Geoscience and Remote Sensing Symposium (IGARSS), pp. 2547-2550, IEEEInt., New York (2011).
43. G. Margarit et al., "On the usage of GRECOSAR, an orbital polarimetric SAR simulator ofcomplex targets, to vessel classification studies," IEEE Trans. Geosci. Remote Sens. 44(12),3517-3526 (2006), http://dx.doi.org/10.1109/TGRS.2006.881120.
44. A. Vedaldi and B. Fulkerson, "VLFeat: an open and portable library of computer visionalgorithms," in Proc. Int. Conf. Multimedia, New York, pp. 1469-1472, ACM, NewYork (2010).